Large-Scale Spectrum Allocation for Cellular Networks via Sparse Optimization

Abstract

This paper studies joint spectrum allocation and user association in large heterogeneous cellular networks. The objective is to maximize some network utility function based on given traffic statistics collected over a slow timescale, conceived to be seconds to minutes. A key challenge is scalability: interference across cells creates dependencies across the entire network, making the optimization problem computationally challenging as the size of the network becomes large. A suboptimal solution is presented, which performs well in networks consisting of one hundred access points (APs) serving several hundred user devices. This is achieved by optimizing over local overlapping neighborhoods, defined by interference conditions, and by exploiting the sparsity of a globally optimal solution. Specifically, with a total of k user devices in the entire network, it suffices to divide the spectrum into k segments, where each segment is mapped to a particular set, or pattern, of active APs within each local neighborhood. The problem is then to find a mapping of segments to patterns, and to optimize the widths of the segments. A convex relaxation is proposed for this, which relies on a re-weighted 1 approximation of an 0 constraint, and is used to enforce the mapping of a unique pattern to each spectrum segment. A distributed implementation based on alternating direction method of multipliers (ADMM) is also proposed. Numerical comparisons with benchmark schemes show that the proposed method achieves a substantial increase in achievable throughput and/or reduction in the average packet delay.

Resource Allocation in Green Dense Cellular Networks: Complexity and Algorithms

“…Their approach iteratively solves a convex optimization problem and an hyper-graph coloring problem. Similar problems to [5] are studied in [7,8]. In [9], the authors study the problem of channel allocation and power control in non-orthogonal multiple access networks.…”

“…They formulate a network utility maximization problem and they use stochastic geometry to obtain the analytical user association bias factors and the channel partition ratios. The works in [5,[7][8][9][10] solve the channel allocation or the user association problem without considering energy harvesting BSs nor the scheduling problem. In [11], the authors study multicast scheduling in cellular networks under deadline constraints.…”

“…return A Z Note that ALG-MCMB calls ALG-MCSB in line 5 where the latter finds the channel allocation according to (7). Based on the analysis of the worst-case complexity of ALG-SCSB 1 , we obtain the worst-case…”

Interference Management and Capacity Analysis for mm-Wave Picocells in Urban Canyons

“…We first characterize intra-cell interference in our system model and then propose a crosslayer approach to deal with it. To this end, we combined techniques from two broad areas that have been studied in the literature: (a) downlink linear precoding and power control [20][21][22][23][24] (b) powerful optimization approaches recently developed for network-level resource allocation [25,26]. Here is a brief description of our two-step method: 1) Given that a resource block is assigned to a pre-defined set of users, we develop a building block at the PHY-layer, which employs an optimal linear method (i.e., LMMSE) for beamforming and power allocation to suppress the LoS intra-cell interference among them.…”

Deep Reinforcement Learning-Based Spectrum Allocation in Integrated Access and Backhaul Networks

“…In [16], it exploits DRL and proposes an intelligent modulation and coding scheme (MCS) selection algorithm for the primary transmission. A similar approach has also been proposed in [8] for dynamic spectrum access in wireless network. However, directly applying deep Q-network (DQN) to the spectrum allocation problem is not feasible, because the action space can be very large with increasing of the network size and available spectrum, and it consumes much longer time for convergence.…”